Determinants of customer satisfaction in online grocery shopping
Bibliographic record
Abstract
This study aims to investigate the factors that influence consumer satisfaction with e-grocery shopping. Moreover, it also delves into factors that motivate shoppers to buy groceries from online retailers rather than conventional stores. A primary survey was administered to collect data. Initially, 500 questionnaires were circulated to respondents. People who have ordered groceries from online sites were the expected respondents. In this study, convenience sampling was used, and data were analyzed using structural equation modeling (SEM). The findings affirmed the relationship between consumer satisfaction and perceived convenience, risk factors, perceived product quality, and time value. However, perceived value and value for the time have a little significant effect on consumer satisfaction. There have been relatively few academic studies that look at the variables that influence customer satisfaction when shopping for groceries online. Most of the studies look at the variables that influence consumer satisfaction in the life insurance and financial services industries. By analyzing the data from Delhi and the Nation Capital Region (NCR) of Delhi, this research aims to bridge this gap available in the literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".